Towards Reliable AI in 6G: Detecting Concept Drift in Wireless Network

TL;DR

This paper introduces two unsupervised, model-agnostic concept drift detectors for 6G wireless networks, improving detection accuracy and reducing false alarms in dynamic environments.

Contribution

It presents novel unsupervised, model-agnostic drift detection methods validated on real-world wireless use cases, outperforming classical detectors.

Findings

Outperform classical detectors by 20-40 percentage points.

Achieve F1-scores of 0.94 and 1.00 in trigger accuracy.

Reduce false alarm rate by up to 20 percentage points.

Abstract

AI-native 6G networks promise unprecedented automation and performance by embedding machine-learning models throughout the radio access and core segments of the network. However, the non-stationary nature of wireless environments due to infrastructure changes, user mobility, and emerging traffic patterns, induces concept drifts that can quickly degrade these model accuracies. Existing methods in general are very domain specific, or struggle with certain type of concept drift. In this paper, we introduce two unsupervised, model-agnostic, batch concept drift detectors. Both methods compute an expected-utility score to decide when concept drift occurred and if model retraining is warranted, without requiring ground-truth labels after deployment. We validate our framework on two real-world wireless use cases in outdoor fingerprinting for localization and for link-anomaly detection, and demonstrate that both methods are outperforming classical detectors such as ADWIN, DDM, CUSUM by 20-40 percentage points. Additionally, they achieve an F1-score of 0.94 and 1.00 in correctly triggering retraining alarm, thus reducing the false alarm rate by up to 20 percentage points compared to the best classical detectors.